The use of fuzzy neural networks for feature/sensor selection

In diagnostic and fuzzy pattern recognition applications it is very difficult to find out which features to use to achieve the optimum performance. This paper describes a PC-based feature selection system that solves this problem. The system uses a real-time fuzzy neural network. By using the numeri...

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Main Author: Ulug, M.E.
Format: Conference Proceeding
Language:English
Subjects:
Computer architecture
Frequency selective surfaces
Fuzzy neural networks
Fuzzy systems
Intelligent sensors
Neural networks
Neurons
Pattern recognition
Sensor phenomena and characterization
Testing
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description In diagnostic and fuzzy pattern recognition applications it is very difficult to find out which features to use to achieve the optimum performance. This paper describes a PC-based feature selection system that solves this problem. The system uses a real-time fuzzy neural network. By using the numerical data about the membership functions and by testing thousands of feature subset combinations, the system searches for a subset that increases the separation between classes. If such a subset exists, its use makes it easier to identify the classes. The use of fewer features also results in smaller array sizes and a faster operation. The results of applying this technique to two different systems are discussed.< >
doi_str_mv 10.1109/MFI.1994.398398
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ispartof Proceedings of 1994 IEEE International Conference on MFI '94. Multisensor Fusion and Integration for Intelligent Systems, 1994, p.607-614
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source IEEE Electronic Library (IEL) Conference Proceedings
subjects Computer architecture
Frequency selective surfaces
Fuzzy neural networks
Fuzzy systems
Intelligent sensors
Neural networks
Neurons
Pattern recognition
Sensor phenomena and characterization
Testing
title The use of fuzzy neural networks for feature/sensor selection
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